DEEP LEARNING FOR AGE GROUP CLASSIFICATION SYSTEM
DOI:
https://doi.org/10.29284/ijasis.4.2.2018.16-22Keywords:
Age group classification, neural network, VGG16, MORPH databaseAbstract
Age Group Classification (AGC) is a difficult task due to variations in human genders, expression, races, poses and so on. The age group analysis is used in multimedia forensic investigation for crime scenes. In this study, an efficient method for AGC is presented. AGC system uses mainly two stage; preprocessing and classification. The preprocessing stage consists of face region detection, gamma correction, Difference Of Gaussian (DOG) filter and normalization. Then the preprocessed images are given to Visual Geometric Group (VGG) 16. The convolution and max-pooling layers in VGG 16 architecture is used to resize the image. The REctified Linear Unit (RELU) is used as an activation function in each convolution and max-pooling layer. The sigmoid layer is used for the AGC into adolescence, adult and senior adult. The system uses MORPH database for the performance evaluation. AGC system produces the classification accuracy of over 90% for all age groups using VGG16 architecture.
References
A.K. Saxena, S. Sharma, and V.K. Chaurasia, “Human age-group estimation using curvelet features”, International Conference on Advances in Computing and Communication Engineering, 2015, pp. 610-615.
S. Izadpanahi and Ö. Toygar, “Geometric feature based age classification using facial images”, IET Conference on Image Processing, 2012, pp. 1-5.
S.H. Lee, and Y.M. Ro, “Local age group modeling in unconstrained face images for facial age classification”, IEEE International Conference on Image Processing, 2014, pp. 1395-1399.
A. Asthana, and S.K. Singh, “Frontal facial images based age classify via SVM”, International Conference on Electronics and Communication Systems, 2015, pp. 644-649.
J.B. Ko, W. Lee, S.E. Choi, and J. Kim, “A gender classification method using age information”, International Conference on Electronics, Information and Communications, 2014, pp. 1-2.
K. Tonchev, I. Paliy, O. Boumbarov, and S. Sokolov, “Human age-group classification of facial images with subspace projection and support vector machines”, IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, 2011, pp. 439-443.
J.H. Jeon, J.Y. Lee, J.S. Bae, and J.O. Kim, “Gradient local binary patterns for age group estimation”, IEEE International Symposium on Consumer Electronics, 2014, pp. 1-2.
A. Yarlagadda, J.V.R. Murthy, and M.K. Prasad, “A novel method for human age group classification based on Correlation Fractal Dimension of facial edges”, Journal of King Saud University-Computer and Information Sciences, Vol. 27, No. 4, 2015, pp. 468-476.
K. Ueki, T. Hayashida, and T. Kobayashi, “Subspace-based age-group classification using facial images under various lighting conditions”, International Conference on Automatic Face and Gesture Recognition, 2017, pp. 6-pp.
W.B. Horng, C.P. Lee, and C.W. Chen, “Classification of age groups based on facial features”, Tamkang Journal of Science and Engineering, Vol. 4, No. 3, pp. 183-192.
J. Jagtap, and M. Kokare, “Human age classification using facial skin analysis and multi-class support vector machine”, International Conference on Signal and Information Processing, 2016, pp. 1-5.
M.A. Hajizadeh, and H. Ebrahimnezhad, “Classification of age groups from facial image using histograms of oriented gradients”, Iranian Conference on Machine Vision and Image Processing, 2011, pp. 1-5.
M.R. Hosen, S. Hasan, M.M. Hasan, and R.K. Das, “Age classification based on EMG signal using Artificial Neural Network”, International Conference on Electrical Engineering and Information Communication Technology, 2015, pp. 1-5.
J. Nithyashri, and G. Kulanthaivel, “Classifying the Human Age Using Discrete Wavelet Transform KNN Classifer and MORPH Database”, Journal of Computer Applications, Vol. 6, No. 4, 2013, pp. 102-106.
K. Ricanek, and T. Tesafaye, “ MORPH: A longitudinal Image Database of normal adult age - progression”, International Conference on Automatic face and Gesture Recognition, 2006, pp. 341- 345.
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